The overall objective of our proposal is to develop with the aid of computers, methods for improving the cost effectiveness and accuracy of diagnostic evaluations of patients with arthritic disorders. The method that we propose to use is based upon pattern recognition techniques using multi-membership classification methods which allows for simultaneous existence of several disorders in any given patient. We have currently identified the relevant features for 14 rheumatic disorders and the conditional probabilities for 187 features in these 14 disorders. The diagnostic software consisting of a preliminary model for updating the probability of diagnosis and for recommending features to be selected for increasing confidence in the diagnosis have already been implemented and are now being tested. The accuracy of our arthritis diagnostic system will be tested with a variety of methods using our existing large rheumatic disease database. The diagnostic system will be evaluated as to its ability to improve the accuracy and cost effectiveness of the diagnostic workup of patients with arthritis. The expense and risk to the patient for the path recommended for diagnosis with the aid of computer will be compared with the judgment of the physician. A number of additional problems which we have encountered will be addressed. These will include methods for deriving conditional probabilities from a large medical database to avoid the bias tests ordered only when they are suspected to be positive, and mathematical methods for evaluating expert subjective estimates of conditional probabilities as compared to consensus or delphian approaches. By simulating clinical diagnostic reasoning, with a modification of classical Bayesian statistics, we hope to greatly enhance physician utilization as well as to increase the clinical usefulness of medical database systems.